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Mesh Saliency via Weakly Supervised Classification-for-Saliency CNN
2019
IEEE Transactions on Visualization and Computer Graphics
Recently, effort has been made to apply deep learning to the detection of mesh saliency. However, one major barrier is to collect a large amount of vertex-level annotation as saliency ground truth for training the neural networks. Quite a few pilot studies showed that this task is difficult. In this work, we solve this problem by developing a novel network trained in a weakly supervised manner. The training is end-to-end and does not require any saliency ground truth but only the class
doi:10.1109/tvcg.2019.2928794
pmid:31329121
fatcat:qacg2u5o7vgaflf2iurexdybna